Small Bird Detection using YOLOv7 with Test-Time Augmentation

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1. Verfasser: Shigematsu, Kosuke
Format: Preprint
Veröffentlicht: 2023
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author Shigematsu, Kosuke
author_facet Shigematsu, Kosuke
contents In this paper, we propose a method specifically aimed at improving small bird detection for the Small Object Detection Challenge for Spotting Birds 2023. Utilizing YOLOv7 model with test-time augmentation, our approach involves increasing the input resolution, incorporating multiscale inference, considering flipped images during the inference process, and employing weighted boxes fusion to merge detection results. We rigorously explore the impact of each technique on detection performance. Experimental results demonstrate significant improvements in detection accuracy. Our method achieved a top score in the Development category, with a public AP of 0.732 and a private AP of 27.2, both at IoU=0.5.
format Preprint
id arxiv_https___arxiv_org_abs_2401_01018
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Small Bird Detection using YOLOv7 with Test-Time Augmentation
Shigematsu, Kosuke
Computer Vision and Pattern Recognition
In this paper, we propose a method specifically aimed at improving small bird detection for the Small Object Detection Challenge for Spotting Birds 2023. Utilizing YOLOv7 model with test-time augmentation, our approach involves increasing the input resolution, incorporating multiscale inference, considering flipped images during the inference process, and employing weighted boxes fusion to merge detection results. We rigorously explore the impact of each technique on detection performance. Experimental results demonstrate significant improvements in detection accuracy. Our method achieved a top score in the Development category, with a public AP of 0.732 and a private AP of 27.2, both at IoU=0.5.
title Small Bird Detection using YOLOv7 with Test-Time Augmentation
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2401.01018